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We consider the problem of forecasting the daily number of hospitalizedCOVID-19 patients at a single hospital site, in order to help administratorswith logistics and planning. We develop several candidate hierarchical Bayesianmodels which directly capture the count nature of data via a generalizedPoisson likelihood, model time-series dependencies via autoregressive andGaussian process latent processes, and share statistical strength acrossrelated sites. We demonstrate our approach on public datasets for 8 hospitalsin Massachusetts, U.S.A. and 10 hospitals in the United Kingdom. Furtherprospective evaluation compares our approach favorably to baselines currentlyused by stakeholders at 3 related hospitals to forecast 2-week-ahead demand byrescaling state-level forecasts.

Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach